"""
Copyright 2017-2019 Fizyr (https://fizyr.com)

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import numpy as np
import random
import warnings

import tensorflow as tf

from ..utils.anchors import (
	anchor_targets_bbox,
	anchors_for_shape,
	parse_anchor_parameters,
	guess_shapes
)

from ..utils.image import (
	TransformParameters,
	adjust_transform_for_image,
	apply_transform,
	preprocess_image,
	resize_image,
)
from ..utils.transform import transform_aabb


class Generator(tf.keras.utils.Sequence):
	""" Abstract generator class.
	"""

	def __init__(
		self,
		transform_generator=None,
		visual_effect_generator=None,
		batch_size=1,
		group_method='ratio',  # one of 'none', 'random', 'ratio'
		shuffle_groups=True,
		image_min_side=800,
		image_max_side=1333,
		transform_parameters=None,
		compute_anchor_targets=anchor_targets_bbox,
		compute_shapes=guess_shapes,
		preprocess_image=preprocess_image,
		anchors_config=None
	):
		""" Initialize Generator object.
		Args
			transform_generator     : A generator used to randomly geometrically transform images and annotations.
			visual_effect_generator : A generator used to randomly visually transform images and annotations.
			batch_size              : The size of the batches to generate.
			group_method            : Determines how images are grouped together (defaults to 'ratio', one of ('none', 'random', 'ratio')).
			shuffle_groups          : If True, shuffles the groups each epoch.
			image_min_side          : After resizing the minimum side of an image is equal to image_min_side.
			image_max_side          : If after resizing the maximum side is larger than image_max_side, scales down further so that the max side is equal to image_max_side.
			transform_parameters    : The transform parameters used for data augmentation.
			compute_anchor_targets  : Function handler for computing the targets of anchors for an image and its annotations.
			compute_shapes          : Function handler for computing the shapes of the pyramid for a given input.
			preprocess_image        : Function handler for preprocessing an image (scaling / normalizing) for passing through a network.
			anchors_config          : Configuration for anchors.
		"""
		self.transform_generator     = transform_generator
		self.visual_effect_generator = visual_effect_generator
		self.batch_size              = int(batch_size)
		self.group_method            = group_method
		self.shuffle_groups          = shuffle_groups
		self.image_min_side          = image_min_side
		self.image_max_side          = image_max_side
		self.transform_parameters    = transform_parameters or TransformParameters()
		self.compute_anchor_targets  = compute_anchor_targets
		self.compute_shapes          = compute_shapes
		self.preprocess_image        = preprocess_image

		self.anchor_params = None
		if anchors_config:
			self.anchor_params = parse_anchor_parameters(anchors_config)

		# Define groups.
		self.group_images()

		# Shuffle when initializing.
		if self.shuffle_groups:
			self.on_epoch_end()

	def __from_config__(
		self,
		config,
		preprocess_image=preprocess_image,
		compute_anchor_targets=anchor_targets_bbox
	):
		""" Initialize Generator object from a configuration.
		Args
			config                 : Configuration for the generator.
			preprocess_image       : Function handler for preprocessing an image (scaling / normalizing) for passing through a network.
			compute_anchor_targets : Function handler for computing the targets of anchors for an image and its annotations.
		"""
		Generator.__init__(
			self,
			transform_generator     = config['transform_generator_class'],
			visual_effect_generator = config['visual_effect_generator_class'],
			batch_size              = config['batch_size'],
			group_method            = config['group_method'],
			shuffle_groups          = config['shuffle_groups'],
			image_min_side          = config['image_min_side'],
			transform_parameters    = config['transform_parameters_class'],
			anchors_config          = config['anchors'],
			preprocess_image        = preprocess_image,
			compute_anchor_targets  = compute_anchor_targets
		)

	def on_epoch_end(self):
		""" If indicated, shuffles the groups at the end of the epoch.
		"""
		if self.shuffle_groups:
			random.shuffle(self.groups)

	def size(self):
		""" Size of the dataset.
		"""
		raise NotImplementedError('size method not implemented')

	def num_classes(self):
		""" Number of classes in the dataset.
		"""
		raise NotImplementedError('num_classes method not implemented')

	def has_label(self, label):
		""" Returns True if label is a known label.
		"""
		raise NotImplementedError('has_label method not implemented')

	def has_name(self, name):
		""" Returns True if name is a known class.
		"""
		raise NotImplementedError('has_name method not implemented')

	def name_to_label(self, name):
		""" Map name to label.
		"""
		raise NotImplementedError('name_to_label method not implemented')

	def label_to_name(self, label):
		""" Map label to name.
		"""
		raise NotImplementedError('label_to_name method not implemented')

	def image_aspect_ratio(self, image_index):
		""" Compute the aspect ratio for an image with image_index.
		"""
		raise NotImplementedError('image_aspect_ratio method not implemented')

	def load_image(self, image_index):
		""" Load an image at the image_index.
		"""
		raise NotImplementedError('load_image method not implemented')

	def load_annotations(self, image_index):
		""" Load annotations for an image_index.
		"""
		raise NotImplementedError('load_annotations method not implemented')

	def load_annotations_group(self, group):
		""" Load annotations for all images in group.
		"""
		annotations_group = [self.load_annotations(image_index) for image_index in group]
		for annotations in annotations_group:
			assert(isinstance(annotations, dict)), '\'load_annotations\' should return a list of dictionaries, received: {}'.format(type(annotations))
			assert('labels' in annotations), '\'load_annotations\' should return a list of dictionaries that contain \'labels\' and \'bboxes\'.'
			assert('bboxes' in annotations), '\'load_annotations\' should return a list of dictionaries that contain \'labels\' and \'bboxes\'.'

		return annotations_group

	def filter_annotations(self, image_group, annotations_group, group):
		""" Filter annotations by removing those that are outside of the image bounds or whose width/height < 0.
		"""
		# Test all annotations.
		for index, (image, annotations) in enumerate(zip(image_group, annotations_group)):
			# Test x2 < x1 | y2 < y1 | x1 < 0 | y1 < 0 | x2 <= 0 | y2 <= 0 | x2 >= image.shape[1] | y2 >= image.shape[0].
			invalid_indices = np.where(
				(annotations['bboxes'][:, 2] <= annotations['bboxes'][:, 0]) |
				(annotations['bboxes'][:, 3] <= annotations['bboxes'][:, 1]) |
				(annotations['bboxes'][:, 0] < 0) |
				(annotations['bboxes'][:, 1] < 0) |
				(annotations['bboxes'][:, 2] > image.shape[1]) |
				(annotations['bboxes'][:, 3] > image.shape[0])
			)[0]

			# Delete invalid indices.
			if len(invalid_indices):
				warnings.warn('Image with id {} (shape {}) contains the following invalid boxes: {}.'.format(
					group[index],
					image.shape,
					annotations['bboxes'][invalid_indices, :]
				))
				for k in annotations_group[index].keys():
					annotations_group[index][k] = np.delete(annotations[k], invalid_indices, axis=0)

		return image_group, annotations_group

	def random_visual_effect_group_entry(self, image, annotations):
		""" Randomly transforms image and annotation.
		"""
		visual_effect = next(self.visual_effect_generator)

		# apply visual effect
		image = visual_effect(image)

		return image, annotations

	def random_visual_effect_group(self, image_group, annotations_group):
		""" Randomly apply visual effect on each image.
		"""
		assert(len(image_group) == len(annotations_group))

		if self.visual_effect_generator is None:
			# do nothing
			return image_group, annotations_group

		for index in range(len(image_group)):
			# apply effect on a single group entry
			image_group[index], annotations_group[index] = self.random_visual_effect_group_entry(
				image_group[index], annotations_group[index]
			)

		return image_group, annotations_group

	def load_image_group(self, group):
		""" Load images for all images in a group.
		"""
		return [self.load_image(image_index) for image_index in group]

	def random_transform_group_entry(self, image, annotations, transform=None):
		""" Randomly transforms image and annotation.
		"""
		# Randomly transform both image and annotations.
		if transform is not None or self.transform_generator:
			if transform is None:
				transform = adjust_transform_for_image(next(self.transform_generator), image, self.transform_parameters.relative_translation)

			# Apply transformation to image.
			image = apply_transform(transform, image, self.transform_parameters)

			# Transform the bounding boxes in the annotations.
			annotations['bboxes'] = annotations['bboxes'].copy()
			for index in range(annotations['bboxes'].shape[0]):
				annotations['bboxes'][index, :] = transform_aabb(transform, annotations['bboxes'][index, :])

		return image, annotations, transform

	def random_transform_group(self, image_group, annotations_group):
		""" Randomly transforms each image and its annotations.
		"""

		assert(len(image_group) == len(annotations_group))

		for index in range(len(image_group)):
			# Transform a single group entry.
			image_group[index], annotations_group[index], _ = self.random_transform_group_entry(image_group[index], annotations_group[index])

		return image_group, annotations_group

	def resize_image(self, image):
		""" Resize an image using image_min_side and image_max_side.
		"""
		return resize_image(image, min_side=self.image_min_side, max_side=self.image_max_side)

	def preprocess_group_entry(self, image, annotations):
		""" Preprocess image and its annotations.
		"""
		# Preprocess the image.
		image = self.preprocess_image(image)

		# Resize image.
		image, image_scale = self.resize_image(image)

		# Apply resizing to annotations too.
		annotations['bboxes'] *= image_scale

		# Convert to the wanted keras floatx.
		image = tf.keras.backend.cast_to_floatx(image)

		return image, annotations

	def preprocess_group(self, image_group, annotations_group):
		""" Preprocess each image and its annotations in its group.
		"""
		assert(len(image_group) == len(annotations_group))

		for index in range(len(image_group)):
			# Preprocess a single group entry.
			image_group[index], annotations_group[index] = self.preprocess_group_entry(image_group[index], annotations_group[index])

		return image_group, annotations_group

	def group_images(self):
		""" Order the images according to self.order and makes groups of self.batch_size.
		"""
		# Determine the order of the images.
		order = list(range(self.size()))
		if self.group_method == 'random':
			random.shuffle(order)
		elif self.group_method == 'ratio':
			order.sort(key=lambda x: self.image_aspect_ratio(x))

		# Divide into groups, one group = one batch.
		self.groups = [[order[x % len(order)] for x in range(i, i + self.batch_size)] for i in range(0, len(order), self.batch_size)]

	def compute_inputs(self, image_group):
		""" Compute inputs for the network using an image_group.
		"""
		# Get the max image shape.
		max_shape = tuple(max(image.shape[x] for image in image_group) for x in range(3))

		# Construct an image batch object.
		image_batch = np.zeros((self.batch_size,) + max_shape, dtype=tf.keras.backend.floatx())

		# Copy all images to the upper left part of the image batch object.
		for image_index, image in enumerate(image_group):
			image_batch[image_index, :image.shape[0], :image.shape[1], :image.shape[2]] = image

		if tf.keras.backend.image_data_format() == 'channels_first':
			image_batch = image_batch.transpose((0, 3, 1, 2))

		return image_batch

	def generate_anchors(self, image_shape):
		""" Generates anchors for an indicated image shape.
		"""
		return anchors_for_shape(image_shape, anchor_params=self.anchor_params, shapes_callback=self.compute_shapes)

	def compute_targets(self, image_group, annotations_group):
		""" Compute target outputs for the network using images and their annotations.
		"""
		# Get the max image shape.
		max_shape = tuple(max(image.shape[x] for image in image_group) for x in range(3))
		anchors   = self.generate_anchors(max_shape)

		batches = self.compute_anchor_targets(
			anchors,
			image_group,
			annotations_group,
			self.num_classes()
		)

		return list(batches)

	def compute_input_output(self, group):
		""" Compute inputs and target outputs for the network.
		"""
		# Load images and annotations.
		image_group       = self.load_image_group(group)
		annotations_group = self.load_annotations_group(group)

		# Check validity of annotations.
		image_group, annotations_group = self.filter_annotations(image_group, annotations_group, group)

		# Randomly apply visual effect.
		image_group, annotations_group = self.random_visual_effect_group(image_group, annotations_group)

		# Randomly transform data.
		image_group, annotations_group = self.random_transform_group(image_group, annotations_group)

		# Perform preprocessing steps.
		image_group, annotations_group = self.preprocess_group(image_group, annotations_group)

		# Compute network inputs.
		inputs = self.compute_inputs(image_group)

		# Compute network targets.
		targets = self.compute_targets(image_group, annotations_group)

		return inputs, targets

	def __len__(self):
		"""
		Number of batches for generator.
		"""

		return len(self.groups)

	def __getitem__(self, index):
		"""
		Keras sequence method for generating batches.
		"""
		group = self.groups[index]
		inputs, targets = self.compute_input_output(group)

		return inputs, targets
